Towards Practical Multimodal Hospital Outbreak Detection

TL;DR

This paper proposes a machine learning-based framework integrating MALDI-TOF, antimicrobial resistance patterns, and electronic health records to enable rapid, cost-effective hospital outbreak detection, reducing reliance on expensive genome sequencing.

Contribution

It introduces a multi-modal, tiered surveillance approach combining diverse data sources for improved outbreak detection in hospitals.

Findings

Multi-modal integration enhances detection accuracy.

The tiered approach reduces dependence on whole genome sequencing.

Identification of high-risk contamination routes linked to clinical procedures.

Abstract

Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility for routine surveillance, especially in less-equipped facilities. We explore three modalities as rapid alternatives: matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry, antimicrobial resistance (AR) patterns, and electronic health records (EHR). We present a machine learning approach that learns discriminative features from these modalities to support outbreak detection. Multi-species evaluation shows that the integration of these modalities can boost outbreak detection performance. We also propose a tiered surveillance paradigm that can reduce the need for WGS through these alternative modalities. Further analysis of EHR information identifies potentially high-risk contamination routes linked to specific clinical procedures, notably those involving invasive equipment and high-frequency workflows, providing infection prevention teams with actionable targets for proactive risk mitigation